Machine learning is a rapidly evolving field that empowers computers to learn independently without explicit programming. At the core of machine learning lies mathematics, which is crucial for expressing ideas within machine learning models.
Linear algebra, an extension of algebra to multiple dimensions, focuses on systems of linear equations. Statistics involves collecting, tabulating, and interpreting numerical data for analysis and presentation, while geometry deals with the measurement and proportions of objects. Calculus studies continuous transitions, known for its analysis of functions’ continuous changes.
Probability and distributions describe the possible values in statistical functions, regression helps estimate relationships between variables, and dimensionality reduction techniques reduce input variables in training data.
In this tutorial, we delve into various mathematical concepts essential for machine learning, progressing from basic to advanced levels with a focus on specific algorithms. By understanding these mathematical principles, one can enhance their grasp of machine learning models and applications.
